Using the LVIS-annotated subset of Objaverse, we introduce 3DCP: an enhancement to the simple, but effective, copy-and-paste technique of[1]}. Figure REF  shows an example of the setup for 3DCP. Here, we render different views of 3D objects and paste them on-top of existing LVIS images. We render 5 distinct views of each object and cache them for use throughout training. During training, an image is selected for the copy-paste augmentation with 0.5 probability, and once selected, 1-3 images of randomly chosen LVIS-annotated Objaverse objects are pasted onto the selected training image. The segmentation masks of the selected objects are added to the training image's annotation as well. Object images and masks are randomly scaled and translated before being pasted. We use this strategy to finetune the pretrained ResNet-50 Mask-RCNN [2]}, [3]} of [4]}. As shown in Tab.REF , simply finetuning this model for 24 epochs yields performance gains across several metrics.
